Rapid transition to telepractice across the lifespan in speech-language pathology: Insight from a survey of clinicians in Canada
Bibliographic record
Abstract
PURPOSE: This study aims to describe the experiences and needs of Canadian speech-language pathologists (SLPs) who conducted communication assessments via telepractice across the lifespan during the first year (2020) of the COVID-19 pandemic. METHOD: The present study consisted of an online survey that aimed to capture both quantitative aspects of telepractice-based communication assessment and the qualitative experience of shifting to telepractice. One hundred sixty-eight practicing SLPs across Canada participated in the survey, between September 2020 and January 2021. Quantitative results were analysed using descriptive statistics while open-ended responses were analysed using thematic analysis. RESULT: SLPs identified challenges and opportunities relating to client and family engagement, access to and knowledge of technology, and the reliability of assessment tools. SLPs also identified a future need for online assessment materials and training, such as materials adapted for different communication needs (e.g. augmentative and alternative communication). CONCLUSION: The present study contributes to a growing understanding worldwide of potential benefits and challenges related to telepractice, fuelled by the necessary shift in practices in our field during the COVID-19 pandemic. The results provide direction for continuing to build a valid and inclusive approach to telepractice in the future.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".